In a lecture given on Friday, Feb. 10 titled “Human Locomotion: How Humans Move Efficiently and Stably,” Dr. Manoj Srinivasan, Associate Professor in Ohio State University's Department of Mechanical Engineering, described experiments on how humans optimize their locomotion behavior under different conditions. Srinivasan also offered explanations of the computational methods used to design robotic prostheses and walking exoskeletons.

New scientific models can qualitatively predict human locomotion under perturbation, but simulating an exact quantitative match is an open problem in the field, Srinivasan said. He added that, even when people are made to walk in manners that they are not habituated to, such as walking sideways, they optimize their speed in a manner that conserves the most energy. While the energy-optimality hypothesis says people usually walk at 1.3 m/s to maximize their energy conservation is an old one, new experiments have shown that the hypothesis holds even under perturbations or deviations, he explained.

Though most studies have been done on constant-speed straight-line locomotion, Srinivasan said there are two that described the optimal human locomotion under different conditions. One study showed that the energy cost for speeding up and slowing down was high. When walking short distances, people tended to walk slower to try to maintain constant speed. The second experiment was done on people walking in complicated curved paths and showed that people tend to slow down when making tight turns because of a stop-twist-stop motion, he added.

Srinivasan, who is also a visiting associate professor at MIT, explained that his lab had validated and modeled the energy-optimality hypothesis under different straight-line conditions, such as slowly increasing treadmill speeds, which forced people to get from point A to point B in limited time. This forced a walk-run mixture, even when the subjects were wearing robotic prosthetics.

He said that, in an effort to model these walks using the metabolic energy-optimizing hypothesis, a new model that has “legs” made of seven rigid segments that move about their joints and 16 muscles (compared to the 40-50 muscles in the actual human leg), was constructed. When given the right constraints, no matter what the initial conditions, the model eventually simulates a walk very similar to the human walk, he explained. Furthermore, the model simulated the muscle forces that an actual human uses, and if the forces were “wild,” the model could be made to behave wildly by walking on tip-toes, etc. he explained.

A recipient of the NSF Career Award, Srinivasan stressed that there were still many open questions in the field of human locomotion and assistive robotics, primarily to develop a single model that quantitatively fits real human locomotion data under all kinds of perturbations and deviations.

“Why did astronauts choose to skip on the moon rather than walk or run, when they could have done any of them?” he asked, adding that it was exactly such questions that a model would hope to answer.

Srinivasan hypothesized that the answer might lie somewhere other than energy-optimal solutions, saying that the energy minimum was rather flat, with little penalty for imperfect optimization. Walks could be calculated to optimize stability, avoid injury and pain, maximize performance such as speed, or any of a number of other criteria, he explained.

The seminar, which was sponsored by the Department of Mechanical and Aerospace Engineering in Maeder Hall, was attended by about 40 people.